Abstract
Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression—one of the most widely used susceptibility models—to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.
Highlights
Most modern landslide susceptibility models aim to predict and map the degree to which landscapes are prone to slope failure
We address whether and how this variability in landslide susceptibility can be captured by multivariate logistic regression, a common model used for statistical inference
We observe that the model performance increases above that of the Discussion Our main goal was to test how sensitive landslide susceptibility estimates are to the choice of where and when we sample predictor data from mapped landslides
Summary
Most modern landslide susceptibility models aim to predict and map the degree to which landscapes are prone to slope failure. One key assumption in this approach is that these environmental predictors are sufficient proxies for the controls on slope instability (Galli et al 2008; Korup 2008; Schulz et al 2018). In susceptibility studies, these causes are routinely subsumed into a number of geological, topographic, climatic, hydrologic, and land-cover variables (Dou et al 2015; Reichenbach et al 2018). Few standards are available to warrant that landslide inventories are comparable (Corominas et al 2013; Golovko et al 2015), and even experts may differ in their subjective assessment and mapping of landslides (Van Den Eeckhaut et al 2005)
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